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Breakthrough Machine Learning Study Published in Marketing Science

By Rachael Yeadon and John Mitchell

Across industries, companies are faced with the challenge of how to sift through the massive amount of readily available user-generated content (UGC) to uncover valuable customer insights. A new study published in Marketing Science describes how to use machine learning to expedite the discovery of customer needs from user-generated content. The details of this approach are published in “Identifying Customer Needs from User-Generated Content” by Artem Timoshenko and John Hauser of the MIT Sloan School. This study is the culmination of the authors’ work over the past several years. Applied Marketing Science (AMS) was instrumental in the development, testing, and refinement of the aforementioned algorithm, and has used the process various times to help clients identify consumer insights.

How does this method work? To start, we work with a client to crystalize their research questions and find the right sources of UGC to analyze. In some cases, good content exists on product review sites, social media pages, customer forums, or blogs. In others, it exists in call center data, transcripts of customer interviews from prior studies, or open-ended survey questions. A trained algorithm sifts through the UGC and pulls out the informative content, which is then processed and analyzed by market research professionals. Clients have praised the machine’s ability to uncover “needle in the haystack” insights that are barely or never mentioned in more traditional qualitative research.

Thanks to this emerging advancement in machine learning, clients in almost every industry are able to uncover customer insights faster and cheaper.